Principle:Langchain ai Langchain Similarity Search

Knowledge Sources	LangChain Approximate Nearest Neighbors
Domains	Vector_Search, Information_Retrieval
Last Updated	2026-02-11 00:00 GMT

Overview

A retrieval technique that finds documents most semantically similar to a query by comparing vector embeddings in a high-dimensional space.

Description

Similarity search embeds the query text, then finds the k nearest document vectors using a distance metric (typically cosine similarity or L2 distance). This enables semantic matching: documents that discuss the same concept are retrieved even if they use different vocabulary.

Usage

Use similarity search as the primary retrieval method in RAG pipelines. Apply metadata filters to narrow results to specific document categories.

Theoretical Basis

$\text{similarity}(q,d)=\cos (\theta )=\frac{\mathbf{𝐪}\cdot \mathbf{𝐝}}{||\mathbf{𝐪}||\cdot ||\mathbf{𝐝}||}$

The top-k documents with highest similarity are returned.

Related Pages

Implemented By

• Implementation:Langchain_ai_Langchain_Chroma_Similarity_Search